JP+PL Dementia and COVID Burnout Neuro-biomarker Project

Project title

"Cross-cultural Data Collection to Elucidate Digital Neuro-Biomarkers of Dementia and Covid-19-Related Mental Burnout"

Japanese team members

• Japanese team PI: Dr. Mihoko OTAKE-MATSUURA, RIKEN AIP

• Research scientist: Dr. Tomasz M. RUTKOWSKI, RIKEN AIP

Polish team members

• Polish team PI: Dr. Tomasz KOMENDZIŃSKI, Nicolaus Copernicus University

AI for Aging and COVID Burnout Preventing Societies - Executive summary

We plan to develop new experimental procedures involving multiple senses (olfactory, visual, auditory, and tactile) to collect rich brainwave data, leading to early prediction of dementia onset using machine learning algorithms [1-3]. 

Our team has planned to create a new experimental approach that involves RIKEN AIP EEG/fNIRS/ET recording and machine learning-based data processing. A collaborative group from NCU will design innovative olfactory, auditory, and visual experiments. These experiments will be conducted with elderly participants from Poland and Japan. All EEG/fNIRS/ET data collected will be processed in Japan, at RIKEN AIP, using machine learning algorithms developed in-house.

References

1. Rutkowski, T.M., Koculak, M., Abe, M.S., and Otake-Matsuura, M. (2019). Brain Correlates of Task–load and Dementia Elucidation with Tensor Machine Learning Using Oddball BCI Paradigm. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing.

2. Rutkowski, T.M., Abe, M.S., Koculak, M., and Otake}, M. Cognitive assessment estimation from behavioral responses in emotional faces evaluation task - AI regression approach for dementia onset prediction in aging societies. In NeurIPS Joint Workshop on AI for Social Good, accepted, in press, ed.

3. Rutkowski, T.M., Zhao, Q., Abe, M.S., and Otake, M. AI neurotechnology for aging societies - task- load and dementia EEG digital biomarker development using information geometry machine learning methods. In AI for Social Good Workshop, pp. 1–4.